Andrew Ng's Recent LinkedIn Posts

Should there be a Stack Overflow for AI coding agents to share learnings with each other? Last week I announced Context Hub (chub), an open CLI tool that gives coding agents up-to-date API documentation. Since then, our GitHub repo has gained over 6K stars, and we've scaled from under 100 to over 1000 API documents, thanks to community contributions and a new agentic document writer. Thank you to everyone supporting Context Hub! OpenClaw and Moltbook showed that agents can use social media built for them to share information. In our new chub release, agents can share feedback on documentation — what worked, what didn't, what's missing. This feedback helps refine the docs for everyone, with safeguards for privacy and security. We're still early in building this out. You can find details and configuration options in the GitHub repo. Install chub as follows, and prompt your coding agent to use it: npm install -g @aisuite/chub GitHub:

Agentic Finance Hackathon in NYC, Nov 15 (submission deadline: Nov 10)! If you’ve been exploring agentic workflows, here’s a concrete way to apply them: Build a pipeline that turns messy financial PDFs into reliable, structured data using Agentic Document Extraction plus your own stack (RAG, vectors, agents). Submit by Nov 10 for a chance to demo in NYC on Nov 15; prizes are $5K, $1K, and an online‑only award. Looking forward to seeing practical, high‑impact applications of agentic AI in finance. Apply: luma.com/jme15h1t 👉 Register and get all the details here: https://luma.com/jme15h1t

Fun breakfast with Yann LeCun. We chatted about open science and open source (grateful for his tireless advocacy of these for decades), JEPA and where AI research and models might go next!.

Will AI create new job opportunities? My daughter Nova loves cats, and her favorite color is yellow. For her 7th birthday, we got a cat-themed cake in yellow by first using Gemini’s Nano Banana to design it, and then asking a baker to create it using delicious sponge cake and icing. My daughter was delighted by this unique creation, and the process created additional work for the baker. Many people are worried about AI taking peoples’ jobs. As a society we have a moral responsibility to take care of people whose livelihoods are harmed. At the same time, I see many opportunities for people to take on new jobs and grow their areas of responsibility. We are still early on the path of AI generating a lot of new jobs. I don't know if baking AI-designed cakes will grow into a large business. (AI Fund is not pursuing this opportunity, because if we do, I will gain a lot of weight.) But throughout history, when people invented tools that unleashed human creativity, large amounts of new and meaningful work have resulted. AI is also growing the demand for many digital services, which can translate into more work for people creating, maintaining, selling, and expanding these services. For example, I used to carry out a limited number of web searches per day. Today, my agents carry out dramatically more web searches. For example, the Agentic Reviewer, which I started as a weekend project and Yixing Jiang helped make much better, automatically reviews research articles. It uses a web search API to search for related work, and this generates vastly more web search queries a day than I have ever done by hand. The evolution of AI and software continues to accelerate, and the set of opportunities for things we can build still grows daily. I’ve stopped writing code by hand. More controversially, I’ve long stopped reading generated code. I realize I’m in the minority here, but I feel I can get built most of what I want without having to look directly at coding syntax, and I operate at a higher level of abstraction using coding agents to manipulate code for me. Will conventional programming languages like Python and TypeScript go the way of assembly — where it gets generated and used, but without direct examination by a human developer — or will models even compile directly from English prompts to byte code? Either way, if every developer becomes 10x more productive, we won't end up with 1/10th as many developers, because the demand for custom software has no practical ceiling. Instead, the number of people who develop software will grow massively. In fact, I’m seeing early signs of “X Engineer” jobs, such as Recruiting Engineer or Marketing Engineer, which are people who sit in a certain business function X to create software for that function. One thing I’m convinced of based on my experience with Nova’s birthday cake: AI will allow us to have a batter life! [Edited for length. Full text: https://lnkd.in/gMHbcdex ]

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems! https://lnkd.in/gC6XGeXK

NeurIPS received 21,575 paper submissions this year. Our Agentic Reviewer, released last week, just surpassed this in number of papers submitted and reviewed. It's clear agentic paper reviewing is here to stay and will be impactful!

New course announcement: Semantic Caching for AI Agents, taught by Tyler Hutcherson and Iliya Zhechev from Redis. Semantic caching can significantly reduce your AI application's inference costs and latency. If someone asks "How do I get a refund?" and another later asks "I want my money back," semantic caching recognizes these mean the same thing so it can use a cached response instead of making another model call. This short course takes you from building your first semantic cache from scratch to implementing production-ready systems using Redis' open-source tools. Skills you'll gain: - Build semantic caches from scratch, then implement them using Redis' SDK with production features - Measure cache performance using hit rate, precision, recall, and latency - Enhance accuracy with threshold tuning, cross-encoders, LLM validation, and fuzzy matching Join and learn to reduce your agentic AI's costs and improve speed! https://lnkd.in/gpFKENEi

Important new course: Agent Skills with Anthropic, built with Anthropic and taught by Elie Schoppik! Skills are constructed as folders of instructions that equip agents with on-demand knowledge and workflows. This short course teaches you how to create them following best practices. Because skills follow an open standard format, you can build them once and deploy across any skills-compatible agent, like Claude Code. What you'll learn: - Create custom skills for code generation and review, data analysis, and research - Build complex workflows using Anthropic's pre-built skills (Excel, PowerPoint, skill creation) and custom skills - Combine skills with MCP and subagents to create agentic systems with specialized knowledge - Deploy the same skills across Claude.ai, Claude Code, the Claude API, and the Claude Agent SDK Join and learn to equip agents with the specialized knowledge they need for reliable, repeatable workflows. https://lnkd.in/g5GPvPjS

New course: Document AI: From OCR to Agentic Doc Extraction, built with LandingAI, where I'm executive chairman, and taught by David Park and Andrea Kropp. Much of the world's data is locked in PDFs, JPEGs, and other documents. This short course shows you how to build agentic workflows that process documents accurately: breaking them into parts, examining each piece carefully, and extracting information through multiple iterations. Traditional Optical Character Recognition (OCR) captures text but loses context from table headers, chart captions, or reading order of columns. After exploring OCR's limitations, you’ll use LandingAI's Agentic Document Extraction (ADE) framework to process documents. ADE treats pages as visually -- as images -- to parse information and extract fields. Skills you'll gain: - Build agents to convert unstructured files into structured Markdown/HTML and JSON - Use ADE to parse complex data like forms, handwriting, or equations - Map extracted information to named fields using a specified schema, with bounding boxes for grounding and validation - Deploy RAG applications with event-driven document processing Come learn about the best tools for processing documents like financial invoices, medical records, or academic papers intelligently: https://lnkd.in/gEnfm3wk

AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful. This is why I increasingly try to select software that lets me control my own data, so I can make it available to my AI agents. Because of AI’s growing capabilities, the value you can now create from “connecting the dots” between different pieces of data is higher than ever. For example, if an email click is logged in one vendor’s system and a subsequent online purchase is logged in a different one, then it is valuable to build agents that can access both of these data sources to see how they correlate to make better decisions. Unfortunately, many SaaS vendors try to create a data silo in their customer’s business. By making it hard for you to extract your data, they create high switching costs. This also allows them to steer you to buy their AI agent services — sometimes at high expense and/or of low quality — rather than build your own or buy from a different vendor. Unfortunately, some SaaS vendors are seeing AI agents coming for this data and working to make it harder for you (and your AI agents) to efficiently access it. One of my teams just told me that a SaaS vendor we have been using to store our customer data wants to charge over $20,000 for an API key to get at our data. This high cost — no doubt intentionally designed to make it hard for customers to get their data out — is adding a barrier to implementing agentic workflows that take advantage of that data. Through AI Aspire (an AI advisory firm), I advise a number of businesses on their AI strategies. When it comes to buying SaaS, I often advise them to try to control their own data (which, sadly, some vendors mightily resist). This way, you can hire a SaaS vendor to record and operate on your data, but ultimately you decide how to route it to the appropriate human or AI system for processing. Over the past decade, a lot of work has gone into organizing businesses’ structured data. Because AI can now process unstructured data much better than before, the value of organizing your unstructured data (including PDF files, which LandingAI’s Agentic Document Extraction specializes in!) is higher than ever before. In the era of generative AI, businesses and individuals have important work ahead to organize their data to be AI-ready. P.S. As an individual, my favorite note-taking app is Obsidian. I am happy to “hire” Obsidian to operate on my notes files. And, all my notes are saved as Markdown files in my file system, and I have built AI agents that read from or write to my Obsidian files. This is a small example of how controlling my own notes data lets me do more with AI agents! [Original text: https://lnkd.in/gYPUvZGT ]

New course announcement: Design, Develop, and Deploy Multi-Agent Systems with CrewAI, taught by João (Joe) Moura, CrewAI Co-founder and CEO. Multi-agent systems let you build AI teams that work together to automate complex workflows, similar to how human teams work. CrewAI makes it simple to build multi-agent systems that handle routine work for you—just define your agents, tasks, and crew, and it manages the complexity of coordinating multiple agents and their context automatically. (Disclosure: I made a small angel investment in CrewAI.) This course takes you from building your first agent to deploying production systems using the open-source CrewAI framework. Skills you'll gain: - Build reliable AI agents equipped with tools, memory, and guardrails - Develop teams of agents that can plan, reason, and coordinate - Deploy production-ready systems with tracing, evaluation, and monitoring Whether you’re exploring multi-agent systems for the first time or looking to take your projects further, this course will help you build a mental framework for designing multi-agent systems, and help you turn ideas into scalable, production-ready applications. Sign up here: https://lnkd.in/gEM_vNFN

If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI. In this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can apply to build a wide variety of applications. If you want to try vibe coding, this will be the best place to start! Further, you'll be able to use these techniques with whatever tool you're most comfortable with (like ChatGPT, Gemini, Claude, or others) -- we're vendor neutral. Skills you'll gain: - How to build web apps with AI - zero coding skills needed - How to fix and improve your creations by chatting with AI - A simple process you can use to build other things you can dream up Building with AI is one of the most fun things in the world. Please join me and take your first step! I think you will be surprised at what you can build. And if you're already an experienced engineer, please share this with someone in your life who's been curious about building with AI. Come build with me! https://lnkd.in/gEA5npAC

How can businesses go beyond using AI for incremental efficiency gains to create transformative impact? I write from the World Economic Forum (WEF) in Davos, Switzerland, where I’ve been speaking with many CEOs about how to use AI for growth. A recurring theme is that running many experimental, bottom-up AI projects — letting a thousand flowers bloom — has failed to lead to significant payoffs. Instead, bigger gains require workflow redesign: taking a broader, perhaps top-down view of the multiple steps in a process and changing how they work together from end to end. Consider a bank issuing loans. The workflow consists of several discrete stages: Marketing -> Application -> Preliminary Approval -> Final Review -> Execution Suppose each step used to be manual. Preliminary Approval used to require an hour-long human review, but a new agentic system can do this automatically in 10 minutes. Swapping human review for AI review — but keeping everything else the same — gives a minor efficiency gain but isn’t transformative. Here’s what would be transformative: Instead of applicants waiting a week for a human to review their application, they can get a decision in 10 minutes. When that happens, the loan becomes a more compelling product, and that better customer experience allows lenders to attract more applications and ultimately issue more loans. However, making this change requires taking a broader business or product perspective, not just a technology perspective. Further, it changes the workflow of loan processing. Switching to offering a “10-minute loan” product would require changing how it is marketed. Applications would need to be digitized and routed more efficiently, and final review and execution would need to be redesigned to handle a larger volume. Even though AI is applied only to one step, Preliminary Approval, we end up implementing not just a point solution but a broader workflow redesign that transforms the product offering. At AI Aspire (an advisory firm I co-lead), here’s what we see: Bottom-up innovation matters because the people closest to problems often see solutions first. But scaling such ideas to create transformative impact often requires seeing how AI can transform entire workflows end to end, not just individual steps, and this is where top-down strategic direction and innovation can help. This year's WEF meeting, as in previous years, has been an energizing event. Among technologists, frequent topics of discussion include Agentic AI (when I coined this term, I was not expecting to see it plastered on billboards and buildings!), Sovereign AI (how nations can control their own access to AI), Talent (the challenging job market for recent graduates, and how to upskill nations), and data-center infrastructure (how to address bottlenecks in energy, talent, GPU chips, and memory). I will address some of these topics in future posts. [Original text: https://lnkd.in/gbiRs2mi ]

I just dumped the latest NVIDIA 10-Q earnings report, released an hour ago, into Agentic Document Extraction, and the results are really accurate! Left side of the image shows the original PDF; right side shows the extracted info, including e.g. the $57.01B revenue in the most recent quarter. This is powered by the DPT (document pre-trained transformer) model. Check it out!

New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra Zuppichini from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks: https://lnkd.in/gmbUriZf

Coursera has entered into a definitive agreement to combine with Udemy. Coursera exists to transform lives through learning, and Udemy -- a company I've long admired -- has done tremendous work upskilling millions. I'm excited about the combination's ability to serve learners. Greg will lead the combined entity as CEO, and I will serve as its Chairman of the board. We look forward to working with the talented Coursera and Udemy teams as well as university partners, institutional partners and instructors. See Greg's post below for more details.

I recently spoke at the Sundance Film Festival on a panel about AI. Sundance is an annual gathering of filmmakers and movie buffs that serves as the premier showcase for independent films in the United States. Knowing that many people in Hollywood are extremely uncomfortable about AI, I decided to immerse myself for a day in this community to learn about their anxieties and build bridges. I’m grateful to Daniel Dae Kim, an actor/producer/director I’ve come to respect deeply for his artistic and social work, for organizing the panel, which also included Daniel, Dan Kwan, Jonathan Wang, and Janet Yang. I found myself surrounded by award-winning filmmakers and definitely felt like the odd person out! First, Hollywood has many reasons to be uncomfortable with AI. People from the entertainment industry come from a very different culture than many who work in tech, and this drives deep differences in what we focus on and what we value. A significant subset of Hollywood is concerned that: - AI companies are taking their work to learn from it without consent and compensation. Whereas the software industry is used to open source and the open internet, Hollywood focuses much more on intellectual property, which underlies the core economic engines of the entertainment industry. - Powerful unions like SAG-AFTRA (Screen Actors Guild-American Federation of Television and Radio Artists) are deeply concerned about protecting the jobs of their members. When AI technology (or any other force) threatens the livelihoods of their members — like voice actors — they will fight mightily against potential job losses. - This wave of technological change feels forced on them more than previous waves, where they felt more free to adopt or reject the technology. For example, celebrities felt like it was up to them whether to use social media. In contrast, negative messaging from some AI leaders who present the technology as unstoppable, perhaps even a dangerous force that will wipe out many jobs, has not encouraged enthusiastic adoption. Having said that, Hollywood is under no illusions that AI will change entertainment, and that if Hollywood does not adapt, perhaps some other place will become the new center for entertainment. The entertainment industry is no stranger to technology change. Radio, TV, computer graphics special effects, video streaming, and social media transformed the industry. But the path to navigating AI’s transformation is still unclear, and organizations like the new Creators Coalition on AI are trying to stake out positions. Unfortunately, Hollywood’s negative sentiment toward AI also means it will produce a lot more Terminator-like movies that portray AI as more dangerous than helpful, and this hurts beneficial AI adoption as well. [Truncated for length. Full text: https://lnkd.in/gFTdRSgB ]

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber R., you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here: https://lnkd.in/gNPY8jbW

An exciting new course: Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-training, taught by Sharon Zhou, PhD, VP of AI at AMD. Available now at DeepLearning.AI. Post-training is the key technique used by frontier labs to turn a base LLM--a model trained on massive unlabeled text to predict the next word/token--into a helpful, reliable assistant that can follow instructions. I've also seen many applications where post-training is what turns a demo application that works only 80% of the time into a reliable system that consistently performs. This course will teach you the most important post-training techniques! In this 5 module course, Sharon walks you through the complete post-training pipeline: supervised fine-tuning, reward modeling, RLHF, and techniques like PPO and GRPO. You'll also learn to use LoRA for efficient training, and to design evals that catch problems before and after deployment. Skills you'll gain: - Apply supervised fine-tuning and reinforcement learning (RLHF, PPO, GRPO) to align models to desired behaviors - Use LoRA for efficient fine-tuning without retraining entire models - Prepare datasets and generate synthetic data for post-training - Understand how to operate LLM production pipelines, with go/no-go decision points and feedback loops These advanced methods aren’t limited to frontier AI labs anymore, and you can now use them in your own applications. Learn here: https://lnkd.in/gn9UAunn

I'm excited to announce Context Hub, an open tool that gives your coding agent the up-to-date API documentation it needs. Install it and prompt your agent to use it to fetch curated docs via a simple CLI. (See image.) Why this matters: Coding agents often use outdated APIs and hallucinate parameters. For example, when I ask Claude Code to call OpenAI's GPT-5.2, it uses the older chat completions API instead of the newer responses API, even though the newer one has been out for a year. Context Hub solves this. Context Hub is also designed to get smarter over time. Agents can annotate docs with notes — if your agent discovers a workaround, it can save it and doesn't have to rediscover it next session. Longer term, we're building toward agents sharing what they learn with each other, so the whole community benefits. Thanks Rohit Prsad and Xin Ye for working with me on this! npm install -g @aisuite/chub GitHub: https://lnkd.in/gGeSApnE

New course: Agent Memory: Building Memory-Aware Agents, built in partnership with Oracle and taught by Richmond Alake @richmondalake and Nacho Martínez Rincón. Many agents work well within a single session but their memory resets once the session ends. Consider a research agent working on dozens of papers across multiple days: without memory, it has no way to store and retrieve what it learned across sessions. This short course teaches you to build a memory system that enables agents to persist memory and thereby learn across sessions. You'll design a Memory Manager that handles different memory types, implement semantic tool retrieval that scales without bloating the context, and build write-back pipelines that let your agent autonomously update and refine what it knows over time. Skills you'll gain: - Build persistent memory stores for different agent memory types - Implement a Memory Manager that orchestrates how your agent reads, writes, and retrieves memory - Treat tools as procedural memory and retrieve only relevant ones at inference time using semantic search Join and learn to build agents that remember and improve over time! https://lnkd.in/gFufR3JD

An exciting new professional certificate: PyTorch for Deep Learning, taught by Laurence Moroney, is now available at DeepLearning.AI. This is the definitive program for learning PyTorch, which is one of the main frameworks researchers use to build breakthrough AI systems. If you want to understand how modern deep learning models work—or build your own custom architectures—PyTorch gives you direct control over the key aspects of model development. This three-course professional certificate takes you from fundamentals through advanced architectures and deployment: Course 1: PyTorch: Fundamentals - Learn how PyTorch represents data with tensors and how datasets fit into the training process. You'll build and train neural networks step by step, monitor training progress, and evaluate performance. By the end, you'll understand PyTorch's workflow and be ready to design, train, and test your own models. Course 2: PyTorch: Techniques and Ecosystem Tools - Master hyperparameter optimization, model profiling, and workflow efficiency. You'll use learning rate schedulers, tackle overfitting, and apply automated tuning with Optuna. Work with TorchVision for visual AI and Hugging Face for NLP. Learn transfer learning and fine-tune pretrained models for new problems. Course 3: PyTorch: Advanced Architectures and Deployment - Build sophisticated architectures including Siamese Networks, ResNet, DenseNet, and Transformers. Learn how attention mechanisms power modern language models and how diffusion models generate images. Prepare models for deployment with ONNX, MLflow, pruning, and quantization. Skills you'll gain: - Build and optimize neural networks in PyTorch—the framework researchers use to create breakthrough models - Fine-tune pretrained models for computer vision and NLP tasks—adapting existing models to solve your specific problems - Implement transformer architectures and work with diffusion models, the core technologies behind ChatGPT and modern image generation - Optimize models with quantization and pruning to make them fast and efficient for real-world deployment Whether you want to use pre-existing models, build your own custom models, or just understand what's happening under the hood of the systems you use, this specialization will give you that foundation. Start learning PyTorch: https://lnkd.in/debGfGct

Hanging out with Project Jupyter co-founder Brian Granger. If not for him and Fernando Pérez, we wouldn’t have the coding notebooks we use daily in AI and Data Science. Very grateful to him and the whole Jupyter team for this wonderful open-source work!

New course: Build and Train an LLM with JAX, built in partnership with Google and taught by Chris Achard. JAX is the open-source library behind Google's Gemini, Veo, and other advanced models. This short course teaches you to build and train a 20-million parameter language model from scratch using JAX and its ecosystem of tools. You'll implement a complete MiniGPT-style architecture from scratch, train it, and chat with your finished model through a graphical interface. Skills you'll gain: - Learn JAX's core primitives: automatic differentiation, JIT compilation, and vectorized execution - Build a MiniGPT-style LLM using Flax/NNX, implementing embedding and transformer blocks - Load a pretrained MiniGPT model and run inference through a chat interface Join and learn this important programming layer for building LLMs! https://lnkd.in/gSYUHCUw

Readers responded with both surprise and agreement last week when I wrote that the single biggest predictor of how rapidly a team makes progress building an AI agent lay in their ability to drive a disciplined process for evals (measuring the system’s performance) and error analysis (identifying the causes of errors). It’s tempting to shortcut these processes and to quickly attempt fixes to mistakes rather than slowing down to identify the root causes. But evals and error analysis can lead to much faster progress. In this first of a two-part letter, I’ll share some best practices for finding and addressing issues in agentic systems. Even though error analysis has long been an important part of building supervised learning systems, it is still underappreciated compared to, say, using the latest and buzziest tools. Identifying the root causes of particular kinds of errors might seem “boring,” but it pays off! If you are not yet persuaded that error analysis is important, permit me to point out:  - To master a composition on a musical instrument, you don’t only play the same piece from start to end. Instead, you identify where you’re stumbling and practice those parts more. - To be healthy, you don’t just build your diet around the latest nutrition fads. You also ask your doctor about your bloodwork to see if anything is amiss. (I did this last month and am happy to report I’m in good health! 😃) - To improve your sports team’s performance, you don’t just practice trick shots. Instead, you review game films to spot gaps and then address them. To improve your agentic AI system, don’t just stack up the latest buzzy techniques that just went viral on social media (though I find it fun to experiment with buzzy AI techniques as much as the next person!). Instead, use error analysis to figure out where it’s falling short, and focus on that. Before analyzing errors, we first have to decide what is an error. So the first step is to put in evals. I’ll focus on that for the remainder of this letter and discuss error analysis next week. If you are using supervised learning to train a binary classifier, the number of ways the algorithm could make a mistake is limited. It could output 0 instead of 1, or vice versa. There is also a handful of standard metrics like accuracy, precision, recall, F1, ROC, etc. that apply to many problems. So as long as you know the test distribution, evals are relatively straightforward, and much of the work of error analysis lies in identifying what types of input an algorithm fails on, which also leads to data-centric AI techniques for acquiring more data to augment the algorithm in areas where it’s weak. With generative AI, a lot of intuitions from evals and error analysis of supervised learning carry over — history doesn’t repeat itself, but it rhymes. [Truncated due to length limit. Full text: https://lnkd.in/gjqv6VeA ]

AI for safety investigations. This will save lives. Thank you Joseph Hanna for your leadership on this and The AES Corporation for partnering with AI Fund to build this.

Another year of rapid AI advances has created more opportunities than ever for anyone — including those just entering the field — to build software. In fact, many companies just can’t find enough skilled AI talent. Every winter holiday, I spend some time learning and building, and I hope you will too. This helps me sharpen old skills and learn new ones, and it can help you grow your career in tech. To be skilled at building AI systems, I recommend that you: - Take AI courses - Practice building AI systems - (Optionally) read research papers Let me share why each of these is important. I’ve heard some developers advise others to just plunge into building things without worrying about learning. This is bad advice! Unless you’re already surrounded by a community of experienced AI developers, plunging into building without understanding the foundations of AI means you’ll risk reinventing the wheel or — more likely — reinventing the wheel badly! For example, during interviews with job candidates, I have spoken with developers who reinvented standard RAG document chunking strategies, duplicated existing evaluation techniques for Agentic AI, or ended up with messy LLM context management code. If they had taken a couple of relevant courses, they would have better understood the building blocks that already exist. They could still rebuild these blocks from scratch if they wished, or perhaps even invent something superior to existing solutions, but they would have avoided weeks of unnecessary work. So structured learning is important! Moreover, I find taking courses really fun. Rather than watching Netflix, I prefer watching a course by a knowledgeable AI instructor any day! At the same time, taking courses alone isn’t enough. There are many lessons that you’ll gain only from hands-on practice. Learning the theory behind how an airplane works is very important to becoming a pilot, but no one has ever learned to be a pilot just by taking courses. At some point, jumping into the pilot's seat is critical! The good news is that by learning to use highly agentic coders, the process of building is the easiest it has ever been. And learning about AI building blocks might inspire you with new ideas for things to build. If I’m not feeling inspired about what projects to work on, I will usually either take courses or read research papers, and after doing this for a while, I always end up with many new ideas. Moreover, I find building really fun, and I hope you will too! [Truncated for length. Full text: https://lnkd.in/gwv8nsgN ]

AI coding just arrived in Jupyter notebooks - and Brian Granger (Jupyter co-founder) and I will show you how to use it. Coding by hand is becoming obsolete. The latest Jupyter AI - built by the Jupyter team and showcased at JupyterCon this week - brings AI assistance directly into notebooks. Most AI coding assistants struggle with Jupyter notebooks. Jupyter AI was designed specifically for them. This is the first course to teach it. In this short course, Brian and I teach you to: - Generate and debug code directly in notebook cells through an integrated chat interface - Provide the right context (like API docs) to help AI write accurate code - Use Jupyter AI's unique notebook features: drag cells to chat, generate cells from chat, attach context for the LLM We've integrated Jupyter AI directly into the DeepLearning.AI platform, so you can start using it immediately. Since Jupyter AI is open source, you can also install and run it locally afterward. Whether you're experienced with notebooks or learning them for the first time, this course will prepare you for AI-assisted notebook development. Start using Jupyter AI (free): https://lnkd.in/gz3r_mRw

After listening to multiple of Harry's 20VC podcasts, it was fun to appear on it. We talked about bottlenecks in AI adoption, US/China geopolitics, what opportunities remain to build in (tldr: a lot), and more!

U.S. policies are driving allies away from using American AI technology. This is leading to interest in sovereign AI — a nation’s ability to access AI technology without relying on foreign powers. This weakens U.S. influence, but might lead to increased competition and support for open source. The U.S. invented the transistor, the internet, and the transformer architecture powering modern AI. It has long been a technology powerhouse. I love America, and am working hard towards its success. But its actions over many years, taken by multiple administrations, have made other nations worry about over reliance on it. In 2022, following Russia’s invasion of Ukraine, U.S. sanctions on banks linked to Russian oligarchs resulted in ordinary consumers’ credit cards being shut off. Shortly before leaving office, Biden implemented “AI diffusion” export controls that limited the ability of many nations — including U.S. allies — to buy AI chips. Under Trump, the “America first” approach has significantly accelerated pushing other nations away. There have been broad and chaotic tariffs imposed on both allies and adversaries. Threats to take over Greenland. An unfriendly attitude toward immigration — an overreaction to the chaos at the southern border during Biden’s administration — including atrocious tactics by ICE (Immigration and Customs Enforcement) that resulted in agents shooting dead Renée Good, Alex Pretti, and others. Global media has widely disseminated videos of ICE terrorizing American cities, and I have highly skilled, law-abiding friends overseas who now hesitate to travel to the U.S., fearing arbitrary detention. Given AI’s strategic importance, nations want to ensure no foreign power can cut off their access. Hence, sovereign AI. Sovereign AI is still a vague, rather than precisely defined, concept. Complete independence is impractical: There are no good substitutes to AI chips designed in the U.S. and manufactured in Taiwan, and a lot of energy equipment and computer hardware are manufactured in China. But there is a clear desire to have alternatives to the frontier models from leading U.S. companies OpenAI, Google, and Anthropic. Partly because of this, open-weight Chinese models like DeepSeek, Qwen, Kimi, and GLM are gaining rapid adoption, especially outside the U.S. When it comes to sovereign AI, fortunately one does not have to build everything. By joining the global open-source community, a nation can secure its own access to AI. The goal isn’t to control everything; rather, it is to make sure no one else can control what you do with it. Indeed, nations use open source software like Linux, Python, and PyTorch. Even though no nation can control this software, no one else can stop anyone from using it as they see fit. [Truncated for length. Full text: https://lnkd.in/g299ZuwG ]

Many people are fighting the growth of data centers because they could increase CO2 emissions, electricity prices, and water use. I’m going to stake out an unpopular view: These concerns are overstated, and blocking data center construction will actually hurt the environment more than it helps. Many politicians and local communities in the U.S. and Europe are organizing to prevent data centers from being built. While data centers impose some burden on local communities, most worries of their harm — such as CO2 emissions, driving up consumer electricity prices, and water use — have been inflated beyond reality, perhaps because many people don't trust AI. Let me address the issues of carbon emissions, electricity prices, and water use in turn. Carbon emissions. Humanity’s growing use of computation is increasing carbon emissions. Data-center operations account for around 1% of global emissions, though this is growing rapidly. At the same time, hyperscalers’ data centers are incredibly efficient for the work they do, and concentrating computation in data centers is far better for the environment than the alternative. For example, many enterprise on-prem compute facilities use whatever power is available on the grid, which might include a mix of older, dirtier energy sources. Hyperscalers use far more renewable energy. On the key metric of PUE (total energy used by a facility divided by amount of energy used for compute; lower is better, with 1.0 being ideal), a typical enterprise on-prem facility might achieve 1.5-1.8, whereas leading hyperscalar data centers achieve a PUE of 1.2 or lower. To be fair, if humanity were to use less compute, we would reduce carbon emissions. But If we are going to use more, data centers are the cleanest way to do it; and computation produces dramatically less carbon than alternatives. Google had estimated that a single web search query produces 0.2 grams of CO2 emissions. In contrast, driving from my home to the local library to look up a fact would generate about 400 grams. Google also recently estimated that the median Gemini LLM app query produces a surprisingly low 0.03 grams of CO2 emissions), and uses less energy than watching 9 seconds of television. AI is remarkably efficient per query — its aggregate impact comes from sheer volume. Major cloud companies continue to push efficiency gains, and the trajectory is promising. Electricity prices. Beyond concerns about energy use, data centers have been criticized for increasing electricity demand and therefore driving up electric utility prices for ordinary consumers. The reality is more complicated. One of the best studies I’ve seen, by Lawrence Berkeley National Laboratory, found that “state-level load growth … has tended to reduce average retail electricity prices.” If a consumer can split the costs of transmission cables with a large data center, then the consumer pays less. [Truncated for length; full text: https://lnkd.in/gQsgNdtH]

Really proud of the DeepLearningAI team. When Cloudflare went down, our engineers used AI coding to quickly implement a clone of basic Cloudflare capabilities to run our site on. So we came back up long before even major websites!

OpenReview is one of the most important pillars supporting AI research and knowledge sharing, through open peer review and publishing. But as a non-profit, it needs our community’s support. Please consider making a donation to this great institution! https://lnkd.in/gEd34Wfe

Separate reports by the publicity firm Edelman and Pew Research (links in orig text, below) show that Americans, and more broadly large parts of Europe and the western world, do not trust AI and are not excited about it. Despite the AI community’s optimism about the tremendous benefits AI will bring, we should take this seriously and not dismiss it. The public’s concerns about AI can be a significant drag on progress, and we can do a lot to address them. According to Edelman’s survey, in the U.S., 49% of people reject the growing use of AI, and 17% embrace it. In China, 10% reject it and 54% embrace it. Pew’s data also shows many other nations much more enthusiastic than the U.S. about AI adoption. Positive sentiment toward AI is a huge national advantage. On the other hand, widespread distrust of AI means: - Individuals will be slow to adopt it. For example, Edelman’s data shows that, in the U.S., those who rarely use AI cite Trust (70%) more than lack of Motivation and Access (55%) or Intimidation by the technology (12%) as an issue. - Valuable projects that need societal support will be stymied. For example, local protests in Indiana brought down Google’s plan to build a data center there. Hampering construction of data centers will hurt AI’s growth. Communities do have concerns about data centers beyond the general dislike of AI; I will address this in a later letter. - Populist anger against AI raises the risk that laws will be passed that hamper AI development. To be clear, all of us working in AI should look carefully at both the benefits and harmful effects of AI (such as deepfakes polluting social media and biased or inaccurate AI outputs misleading users), speak truthfully about both benefits and harms, and work to ameliorate problems even as we work to grow the benefits. But hype about AI’s danger has done real damage to trust in our field. Much of this hype has come from leading AI companies that aim to make their technology seem extraordinarily powerful by, say, comparing it to nuclear weapons. Unfortunately, a significant fraction of the public has taken this seriously and thinks AI could bring about the end of the world. The AI community has to stop self-inflicting these wounds and work to win back society’s trust. Where do we go from here? First, to win people’s trust, we have a lot of work ahead to make sure AI broadly benefits everyone. “Higher productivity” is often viewed by general audiences as a codeword for “my boss will make more money,” or worse, layoffs. As amazing as ChatGPT is, we still have a lot of work to do to build applications that make an even bigger positive impact on people’s lives. I believe providing training to people will be a key piece of the puzzle. DeepLearning.AI will continue to lead the charge on AI training, but we will need more than this. [Truncated for length. Full text, with links: https://lnkd.in/gUgMDMGS ]

Is there an AI bubble? With the massive number of dollars going into AI infrastructure such as OpenAI’s $1.4 trillion plan and Nvidia briefly reaching a $5 trillion market cap, many have asked if speculation and hype have driven the values of AI investments above sustainable values. However, AI isn’t monolithic, and different areas look bubbly to different degrees. - AI application layer: There is underinvestment. The potential is still much greater than most realize. - AI infrastructure for inference: This still needs significant investment. - AI infrastructure for model training: I’m still cautiously optimistic about this sector, but there could also be a bubble. Caveat: I am absolutely not giving investment advice! AI application layer. There are many applications yet to be built over the coming decade using new AI technology. Almost by definition, applications that are built on top of AI infrastructure/technology (such as LLM APIs) have to be more valuable than the infrastructure, since we need them to be able to pay the infrastructure and technology providers. I am seeing many green shoots across many businesses that are applying agentic workflows, and am confident this will grow. I have also spoken with many Venture Capital investors who hesitate to invest in AI applications because they feel they don’t know how to pick winners, whereas the recipe for deploying $1B to build AI infrastructure is better understood. Some have also bought into the hype that almost all AI applications will be wiped out merely by frontier LLM companies improving their foundation models. Overall, I believe there is significant underinvestment in AI applications. This area remains a huge focus for my venture studio, AI Fund. AI infrastructure for inference. Despite AI’s low penetration today, infrastructure providers are already struggling to fulfill demand for processing power to generate tokens. Several of my teams are worried about whether we can get enough inference capacity, and both cost and inference throughput are limiting our ability to use even more. It is a good problem to have that businesses are supply-constrained rather than demand-constrained. The latter is a much more common problem, when not enough people want your product. But insufficient supply is nonetheless a problem, which is why I am glad our industry is investing significantly in scaling up inference capacity. As one concrete example of high demand for token generation, highly agentic coders are progressing rapidly. I’ve long been a fan of Claude Code; OpenAI Codex also improved dramatically with the release of GPT-5; and Gemini 3 has made Google CLI very competitive. As these tools improve, their adoption will grow. At the same time, overall market penetration is still low, and many developers are still using older generations of coding tools (and some aren’t even using any agentic coding tools). [Truncated for length. Full text: https://lnkd.in/gnMYckzB ]

To all SF Bay Area recruiting professionals: I've been thinking about how to help more people get jobs, and how AI will change the recruiting profession. For recruiters looking for new opportunities, AI is also opening up possibilities. I'm organizing an in-person event for Recruiting Professionals in Mountain View this Friday Dec 5th, 6-8pm to share ideas and to learn from each other. If you’re nearby and interested, please apply to attend:

Sharing a fun recipe for building a highly autonomous, moderately capable, and very UNreliable agent using the open source aisuite package that Rohit Prasad and I have been working on. With a few lines of code, you can give a frontier LLM a tool (like disk access or web search), prompt it with a high-level task (such as creating a snake game and saving as an HTML file, or carrying out deep research), and let the LLM loose and see what it does. Example in image. Caveat: This is not how practical agents are built today, since most need much more scaffolding (see my Agentic AI course to learn more), but is still interesting to experiment with. Longer write-up here: https://lnkd.in/g3HK6iRA

As amazing as LLMs are, improving their knowledge today involves a more piecemeal process than is widely appreciated. I’ve written before about how AI is amazing... but not that amazing. Well, it is also true that LLMs are general... but not that general. We shouldn’t buy into the inaccurate hype that LLMs are a path to AGI in just a few years, but we also shouldn’t buy into the opposite, also inaccurate hype that they are only demoware. Instead, I find it helpful to have a more precise understanding of the current path to building more intelligent  models. First, LLMs are indeed a more general form of intelligence than earlier generations of technology. This is why a single LLM can be applied to a wide range of tasks. The first wave of LLM technology accomplished this by training on the public web, which contains a lot of information about a wide range of topics. This made their knowledge far more general than earlier algorithms that were trained to carry out a single task such as predicting housing prices or playing a single game like chess or Go. However, they’re far less general than human abilities. For instance, after pretraining on the entire content of the public web, an LLM still struggles to adapt to write in certain styles that many editors would be able to, or use simple websites reliably. After leveraging pretty much all the open information on the web, progress got harder. Today, if a frontier lab wants an LLM to do well on a specific task — such as code using a specific programming language, or say sensible things about a specific niche in, say, healthcare or finance — researchers might go through a laborious process of finding or generating lots of data for that domain and then preparing that data (cleaning low-quality text, deduplicating, paraphrasing, etc.) to create data to give an LLM that knowledge. Or, to get a model to perform certain tasks, such as use a web browser, developers might go through an even more laborious process of creating many RL gyms (simulated environments) to let an algorithm repeatedly practice a narrow set of tasks. A typical human, despite having seen vastly less text or practiced far less in computer-use training environments than today's  frontier models, nonetheless can generalize to a far wider range of tasks than a frontier model. Humans might do this by taking advantage of continuous learning from feedback, or by having superior representations of non-text input (the way LLMs tokenize images still seems like a hack to me), and many other mechanisms that we do not yet understand. [Truncated for length; full text: https://lnkd.in/gQUHwrFu ]

Releasing a new "Agentic Reviewer" for research papers. I started coding this as a weekend project, and Yixing J. made it much better. I was inspired by a student who had a paper rejected 6 times over 3 years. Their feedback loop -- waiting ~6 months for feedback each time -- was painfully slow. We wanted to see if an agentic workflow can help researchers iterate faster. When we trained the system on ICLR 2025 reviews and measured Spearman correlation (higher is better) on the test set: - Correlation between two human reviewers: 0.41 - Correlation between AI and a human reviewer: 0.42 This suggests agentic reviewing is approaching human-level performance. The agent grounds its feedback by searching arXiv, so it works best in fields like AI where research is freely published there. It’s an experimental tool, but I hope it helps you with your research. Check it out here: http://paperreview.ai

DeepLearning.AI Pro is now generally available -- this is the one membership that keeps you at the forefront of AI. Please join! There has never been a moment when the distance between having an idea and building it has been smaller. Things that required months of work for teams can now be built by individuals using AI, in days. This is why we built DeepLearning.AI Pro. I'm personally working hard on this membership program to help you to build applications that can launch or accelerate your career, and shape the future of AI. DeepLearning.AI Pro gives you full access to 150+ programs, including my recently launched Agentic AI course, the new Post-Training and PyTorch courses by Sharon Zhou and Laurence Moroney (just released this week), and all of DeepLearning.AI's top courses and professional certificates. All course videos remain free. Pro membership adds hands-on learning: labs to build working systems, practice questions to hone your understanding, and certificates to share your skills. I'm also building new tools to help you create AI applications and grow your career (and have fun doing so!). Many will be available first to Pro members. Try out DeepLearning.AI Pro free, and let me know what you build!

Happy 2026! Will this be the year we finally achieve AGI? I’d like to propose a new version of the Turing Test, which I’ll call the Turing-AGI Test, to see if we’ve achieved this. I’ll explain in a moment why having a new test is important. The public thinks achieving AGI means computers will be as intelligent as people and be able to do most or all knowledge work. I’d like to propose a new test. The test subject — either a computer or a skilled professional human — is given access to a computer that has internet access and software such as a web browser and Zoom. The judge will design a multi-day experience for the test subject, mediated through the computer, to carry out work tasks. For example, an experience might consist of a period of training (say, as a call center operator), followed by being asked to carry out the task (taking calls), with ongoing feedback. This mirrors what a remote worker with a fully working computer (but no webcam) might be expected to do. A computer passes the Turing-AGI Test if it can carry out the work task as well as a skilled human. Most members of the public likely believe a real AGI system will pass this test. Surely, if computers are as intelligent as humans, they should be able to perform work tasks as well as a human one might hire. Thus, the Turing-AGI Test aligns with the popular notion of what AGI  means. Here’s why we need a new test: “AGI” has turned into a term of hype rather than a term with a precise meaning. A reasonable definition of AGI is AI that can do any intellectual task that a human can. When businesses hype up that they might achieve AGI within a few quarters, they usually try to justify these statements by setting a much lower bar. This mismatch in definitions is harmful because it makes people think AI is becoming more powerful than it actually is. I’m seeing this mislead everyone from high-school students (who avoid certain fields of study because they think it’s pointless with AGI’s imminent arrival) to CEOs (who are deciding what projects to invest in, sometimes assuming AI will be more capable in 1-2 years than any likely reality). The original Turing Test, which required a computer to fool a human judge, via text chat, into being unable to distinguish it from a human, has been insufficient to indicate human-level intelligence. The Loebner Prize competition actually ran the Turing Test and found that being able to simulate human typing errors — perhaps even more than actually demonstrating intelligence — was needed to fool judges. A main goal of AI development today is to build systems that can do economically useful work, not fool judges. Thus a modified test that measures ability to do work would be more useful than a test that measures the ability to fool humans. [Truncated for length. Full text: https://lnkd.in/gSn2KB3R ]

I recently received an email titled “An 18-year-old’s dilemma: Too late to contribute to AI?” Its author, who gave me permission to share this, is preparing for college. He is worried that by the time he graduates, AI will be so good there’s no meaningful work left for him to do to contribute to humanity, and he will just live on Universal Basic Income (UBI). I wrote back to reassure him that there will still be plenty of work he can do for decades hence, and encouraged him to work hard and learn to build with AI. But this conversation struck me as an example of how harmful hype about AI is. Yes, AI is amazingly intelligent, and I’m thrilled to be using it every day to build things I couldn’t have built a year ago. At the same time, AI is still incredibly dumb, and I would not trust a frontier LLM by itself to prioritize my calendar, carry out resumé screening, or choose what to order for lunch — tasks that businesses routinely ask junior personnel to do. Yes, we can build AI software to do these tasks. For example, after a lot of customization work, one of my teams now has a decent AI resumé screening assistant. But the point is it took a lot of customization. Even though LLMs can handle a much more general set of tasks than previous iterations of AI technology, compared to what humans can do, they are still highly specialized. They’re much better at working with text than other modalities, still require lots of custom engineering to get it the right context for a particular application, and we have few tools — and only inefficient ones — for getting our systems to learn from feedback and repeated exposure to a specific task (such as screening resumés for a particular role). AI has stark limitations, and despite rapid improvements, it will remain limited compared to humans for a long time. AI is amazing, but it has unfortunately been hyped up to be even more amazing than it is. A pernicious aspect of hype is that it often contains an element of truth, but not to the degree of the hype. This makes it difficult for nontechnical people to discern where the truth really is. Modern AI is a general purpose technology that is enabling many applications, but AI that can do any intellectual tasks that a human can (a popular definition for AGI) is still decades away or longer. This nuanced message that AI is general, but not that general, often is lost in the noise of today's media environment. [Truncated for length. Full text:  https://lnkd.in/gAuQcZ8M ]

New course: Gemini CLI: Code & Create with an Open-Source Agent, built with Google and taught by Jack Wotherspoon. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: - Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources - Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace - Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable - this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster: https://lnkd.in/gMQXVnFu

Job seekers in the U.S. and many other nations face a tough environment. At the same time, fears of AI-caused job loss have — so far — been overblown. However, the demand for AI skills is starting to cause shifts in the job market. I’d like to share what I’m seeing on the ground. First, many tech companies have laid off workers over the past year. While some CEOs cited AI as the reason — that AI is doing the work, so people are no longer needed — the reality is AI just doesn’t work that well yet. Many of the layoffs have been corrections for overhiring during the pandemic or general cost-cutting and reorganization that occasionally happened even before modern AI. Outside of a handful of roles, few layoffs have resulted from jobs being automated by AI. Granted, this may grow in the future. People who are currently in some professions that are highly exposed to AI automation, such as call-center operators, translators, and voice actors, are likely to struggle to find jobs and/or see declining salaries. But widespread job losses have been overhyped. Instead, a common refrain applies: AI won’t replace workers, but workers who use AI will replace workers who don’t. For instance, because AI coding tools make developers much more efficient, developers who know how to use them are increasingly in-demand. (If you want to be one of these people, please take our short courses on Claude Code, Gemini CLI, and Agentic Skills!) So AI is leading to job losses, but in a subtle way. Some businesses are letting go of employees who are not adapting to AI and replacing them with people who are. This trend is already obvious in software development. Further, in many startups’ hiring patterns, I am seeing early signs of this type of personnel replacement in roles that traditionally are considered non-technical. Marketers, recruiters, and analysts who know how to code with AI are more productive than those who don’t, so some businesses are slowly parting ways with employees that aren’t able to adapt. I expect this will accelerate. At the same time, when companies build new teams that are AI native, sometimes the new teams are smaller than the ones they replace. AI makes individuals more effective, and this makes it possible to shrink team sizes. For example, as AI has made building software easier, the bottleneck is shifting to deciding what to build — this is the Product Management (PM) bottleneck. A project that used to be assigned to 8 engineers and 1 PM might now be assigned to 2 engineers and 1 PM, or perhaps even to a single person with a mix of engineering and product skills. The good news for employees is that most businesses have a lot of work to do and not enough people to do it. People with the right AI skills are often given opportunities to step up and do more. [Truncated for length. Full text; https://lnkd.in/gJC5sbVA ]

I just got back from AI Dev x NYC, the AI developer conference where our community gathers for a day of coding, learning, and connecting. The vibe in the room was buzzing! It was at the last AI Dev in San Francisco that I met up with Kirsty Tan and started collaborating with her on what became our AI advisory firm AI Aspire. In-person meetings can spark new opportunities, and I hope the months to come will bring more stories about things that started in AI Dev x NYC! The event was full of conversations about coding with AI, agentic AI, context engineering, governance, and building and scaling AI applications in startups and in large corporations. But the overriding impression I took away was one of near-universal optimism about our field, despite the mix of pessimism and optimism about AI in the broader world. For example, many businesses have not yet gotten AI agents to deliver a significant ROI, and some AI skeptics are quoting an MIT study that said 95% of AI pilots are failing. (This study, by the way, has methodological flaws that make the viral headline misleading; see link in original post.) But at AI Dev were many of the teams responsible for the successful and rapidly growing set of AI applications. Speaking with fellow developers, I realized that because of AI's low penetration in businesses, it is simultaneously true that (a) many businesses do not yet have AI delivering significant ROI, and (b) many skilled AI teams are starting to deliver significant ROI and see the number of successful AI projects climbing rapidly, albeit from a low base. This is why AI developers are bullish about the growth that is to come. Multiple exhibitors told me this was the best conference they had attended in a long time, because they got to speak with real developers. One told me that many other conferences seemed like fluff, whereas participants at AI Dev had much deeper technical understanding and thus were interested in and able to understand the nuances of cutting-edge technology. Whether the discussion was on observability of agentic workflows, the nuances of context engineering for AI coding, or a debate on how long the proliferation of RL gyms for training LLMs will continue, there was deep technical expertise in the room that lets us collectively see further into the future. One special moment for me was when Nick Thompson, moderating a panel with Miriam Vogel and me, asked about governance. I replied that the United States’ recent hostile rhetoric toward immigrants is one of the worst moves it is making, and many in the audience clapped. Nick spoke about this moment in a video (links in original post). [Truncated for length; full text with links: https://lnkd.in/gQhdiY8B ]

Apple just named its latest laptop Neo -- same name as my son! Should I buy one? If I run Amazon Nova on an Apple Neo I hope to blow both of my kids' minds.

The full agenda for AI Dev 25 x NYC is ready. Developers from Google, AWS, Vercel, Groq, Mistral AI, SAP, and other exciting companies will share what they've learned building production AI systems. Here's what we'll cover: Agentic Architecture: When orchestration frameworks help versus when they accumulate errors. How model-driven agents and autonomous planning handle edge cases. Context Engineering: Why retrieval fails for complex reasoning tasks. How knowledge graphs connect information that vector search misses. Building memory systems that preserve relationships. Infrastructure: Where hardware, models, and applications create scaling bottlenecks. Semantic caching strategies that cut costs and latency. How inference speed enables better orchestration. Production Readiness: Moving from informal evaluation to systematic agent testing. Translating AI governance into engineering practice. Building under regulatory constraints. Tooling: MCP implementations that work. Context-rich code review systems. Working demos you can adapt for your applications. I'll share my perspective on where AI development is heading. Looking forward to seeing you there!

The next leap in AI isn’t only about bigger models. It’s also about smarter training. Together with DeepLearning.AI we’re helping developers master fine-tuning and reinforcement learning using the post-training techniques that power today’s frontier LLMs. Learn from Sharon Zhou, PhD, VP of AI at AMD, and train smarter with AMD GPUs. 🔗 Enroll now: https://lnkd.in/gM9tSdQa

New course: A2A: The Agent2Agent Protocol, built with Google and IBM, and taught by Holt S., Ivan 🥁 Nardini, and Sandi Besen. Connecting agents built with different frameworks usually requires extensive custom integration. This short course teaches you A2A, the open protocol standardizing how agents discover each other and communicate. Since IBM’s ACP (Agent Communication Protocol) joined forces with A2A, A2A has emerged as the industry standard. In this course, you'll build a healthcare multi-agent system where agents built with different frameworks, such as Google ADK (Agent Development Kit) and LangGraph, collaborate through A2A. You'll wrap each agent as an A2A server, build A2A clients to connect to them, and orchestrate them into sequential and hierarchical workflows. Skills you'll gain: - Expose agents from different frameworks as A2A servers to make them discoverable and interoperable - Chain A2A agents sequentially using ADK, where one agent's output feeds into the next - Connect A2A agents to external data sources using MCP (Model Context Protocol) - Deploy A2A agents using Agent Stack, IBM's open-source infrastructure Join and learn the protocol standardizing agent collaboration! https://lnkd.in/gsTRYyrh

Learn to build your own voice-activated AI assistant that can execute tasks like gathering recent AI news from the web, scripting out a podcast, and using tools to put all that into a multi-speaker podcast. See our new short course: "Building Live Voice Agents with Google’s ADK (Agent Development Kit),” taught by Google’s Lavi Nigam and Sita Lakshmi Sangameswaran. ADK provides modular components that make it easy to build and debug agents. It also includes a built-in web interface for tracing agentic reasoning. This course illustrates these concepts via building a live voice agent that can chain actions to complete a complex task like creating a podcast. This requires maintaining context, implementing guardrails, reasoning, and handling audio streaming, while keeping latency low. You’ll learn to: - Build voice agents that listen, reason, and respond - Guide your agent to follow a specific workflow to accomplish a task - Coordinate specialized agents to build an agentic podcast workflow that researches topics and produces multi-speaker audio - Understand how to deploy an agent into production Even if you’re not yet building voice systems, you'll find understanding how realtime agents stream data and maintain reliability useful for designing modern agentic applications. Please join here: https://lnkd.in/ga6tD5rt